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- W2030793934 abstract "Knowledge of structural class plays an important role in understanding protein folding patterns. In this study, a simple and powerful computational method, which combines support vector machine with PSI-BLAST profile, is proposed to predict protein structural class for low-similarity sequences. The evolution information encoding in the PSI-BLAST profiles is converted into a series of fixed-length feature vectors by extracting amino acid composition and dipeptide composition from the profiles. The resulting vectors are then fed to a support vector machine classifier for the prediction of protein structural class. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence similarity lower than 40% and 25%, respectively. The overall accuracies attain 70.7% and 72.9% for 1189 and 25PDB datasets, respectively. Comparison of our results with other methods shows that our method is very promising to predict protein structural class particularly for low-similarity datasets and may at least play an important complementary role to existing methods." @default.
- W2030793934 created "2016-06-24" @default.
- W2030793934 creator A5011641116 @default.
- W2030793934 creator A5021047181 @default.
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- W2030793934 date "2010-10-01" @default.
- W2030793934 modified "2023-10-14" @default.
- W2030793934 title "Prediction of protein structural class for low-similarity sequences using support vector machine and PSI-BLAST profile" @default.
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- W2030793934 doi "https://doi.org/10.1016/j.biochi.2010.06.013" @default.
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